Edge device, connection establishment system, connection establishment method, and non-transitory computer-readable medium

ABSTRACT

An edge device that automatically establishes a connection with a remote monitoring device when an abnormality has occurred in a plant is provided. The edge device includes a controller configured to detect an abnormality on the basis of data regarding an observation target device, and a connection establishment unit configured to establish a connection with a remote monitoring device such that the remote monitoring device is able to remotely access the edge device when the controller has detected the abnormality and determined that the detected abnormality matches a predetermined condition, wherein the remote access is a connection for remotely checking a state of the observation target device, or for enabling a remote manipulation of the observation target device.

TECHNICAL FIELD

The present disclosure relates to an edge device, a connectionestablishment system, a connection establishment method, and a program.Priority is claimed on Japanese Patent Application No. 2018-212479,filed Nov. 12, 2018, the content of which is incorporated herein byreference.

BACKGROUND ART

Machines with rotating parts such as compressors, pumps, and papermachines, paper converting machines are often used in plants. So-calledoperation data such as vibration data obtained from these machines isimportant data regarding operation of plants. Therefore, plant operatorsare often afraid of leakage of the operation data to the outside andconnections between plants and external networks are often cut off.Patent Literature 1 discloses a technology that provides a securitygateway to select data transmission information according to asituation.

CITATION LIST Patent Literature [Patent Literature 1]

Japanese Unexamined Patent Application, First Publication No.2016-034066

SUMMARY

In the related art, if a machine malfunctions in a plant that has beendisconnected from an external network, a person in charge of repair,maintenance, or operation, has to go directly to the site.

It is an object of at least one embodiment of the present disclosure toprovide an edge device, a connection establishment system, a connectionestablishment method, and a program that can automatically establish aconnection with a remote monitoring device such that remote access fromthe remote monitoring device is made possible only when an abnormalityhas occurred in a plant.

According to an aspect of the present disclosure, an edge deviceincludes a controller configured to detect an abnormality on the basisof data regarding an observation target device, and a connectionestablishment unit configured to establish a connection with a remotemonitoring device such that the remote monitoring device is able toremotely access the edge device when the controller has detected theabnormality and determined that the detected abnormality matches apredetermined condition, wherein the remote access is a connection forremotely checking a state of the observation target device, or forenabling a remote manipulation of the observation target device.

According to at least one of the above aspects, only when an abnormalityhas occurred in the plant, remote access from the remote monitoringdevice is made possible such that appropriate countermeasures can betaken even if a person in charge of repair does not go to the site.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram showing an overall configuration of a connectionestablishment system including an edge device according to a firstembodiment.

FIG. 2 is a diagram showing a functional configuration of a plantfacility according to the first embodiment.

FIG. 3 is a diagram showing a functional configuration of the edgedevice according to the first embodiment.

FIG. 4 is a diagram showing a process flow of the edge device accordingto the first embodiment.

FIG. 5 is a diagram showing an overall configuration of a connectionestablishment system including an edge device according to a secondembodiment.

FIG. 6 is a diagram showing a process flow of the edge device accordingto the second embodiment.

DESCRIPTION OF EMBODIMENTS First Embodiment

Hereinafter, an edge device 4 according to a first embodiment will bedescribed with reference to FIGS. 1 to 4.

Overall Configuration

FIG. 1 is a diagram showing an overall configuration of a connectionestablishment system 1 including the edge device 4 according to thefirst embodiment.

The connection establishment system 1 includes a plant system 2, arouter 6, an external network 7, a connection server 8, and a remotemonitoring device 9. The plant system 2 includes a plant facility 3 andthe edge device 4. The plant facility 3 includes, for example, a machinewith a rotating part such as a compressor, a pump, a paper machine, andpaper converting machine.

The edge device 4 is connected to the plant facility 3 and the router 6.The connection between them may be wireless or wired. The router 6 maybe a mobile router. The edge device 4 includes a function of analyzingdata collected by the plant facility 3. Therefore, the edge device 4 isconfigured such that it is not accessible directly from customer PCs 5Aand 5B. That is, the customer PCs 5A and 5B do not know an ID andpassword for connecting to the edge device 4 and only the remotemonitoring device 9 knows the ID and password.

On the other hand, if the remote monitoring device 9 can freely connectto the edge device 4 by a remote desktop (RDP) or the like, the remotemonitoring device 9 can freely acquire data of the plant facility 3. Inorder to prevent this, the customer PCs 5A and 5B have permission forconnection by an RDP or the like. Thus, the remote monitoring device 9can connect to the edge device 4 by an RDP or the like only when thecustomer PCs 5A and 5B permit connection.

Further, the customer PCs 5A and 5B can access a web application of aweb server in the edge device 4 by http. Accordingly, the customer PCs5A and 5B can read results of analysis by the edge device 4 by accessingthe web application by http.

The customer PCs 5A and 5B are connected to the external network 7.Although only two customer PCs 5A and 5B are illustrated in FIG. 1, thenumber of customer PCs similar to the customer PCs 5A and 5B may be oneor three or more. The customer PCs 5A and 5B may be portable terminalssuch as tablet terminals, smartphones, mobile phones, or laptopcomputers or may be any fixed terminals including desktop personalcomputers. Further, the customer PCs 5A and 5B may access the edgedevice 4 by the same method as the remote monitoring device 9 accessesthe edge device 4 by an RDP or the like or may access it by anothermethod.

The router 6 functions as a communication path that connects the plantsystem 2 and the external network 7. The router 6 is connected to theconnection server 8 and the remote monitoring device 9 via the externalnetwork 7. The connection between them may be wireless or wired. Theconnection server 8 is a server for realizing secure communicationbetween the plant system 2 and the remote monitoring device 9 and securecommunication between the plant system 2 and the customer PCs 5A and 5B.The connection server 8 may realize secure communication, for example,using the technology of Weaved, Inc. (currently remot3.it) (US PatentApplication Publication No. 2016/0344745). However, the connectionserver 8 may realize secure communication using other technologieswithout being limited to that of the above embodiment.

Functional Configuration of Plant Facility

FIG. 2 is a diagram showing a functional configuration of the plantfacility 3 according to the first embodiment.

The plant facility 3 includes a control device 31, a plant machine 32with a rotating part, and a sensor 321. The plant machine 32 with arotating part may be, but is not limited to, a compressor, a pump, or apaper machine. Further, the number of plant machines 32 may be two ormore. The control device 31 controls the plant machine 32. The sensor321 is attached to the plant machine 32. The sensor 321 observesvibration data of the plant machine 32 and transmits the observed datato a database 44 in the edge device 4. The number of sensors 321attached to the plant machine 32 may be two or more. Data observed bythe sensor 321 is described herein as vibration data. However, theobserved data is not limited to vibration data and may be various dataobserved by various sensors or may be data acquired from a distributedcontrol system (DCS).

Functional Configuration of Edge Device

FIG. 3 is a diagram showing a functional configuration of the edgedevice 4 according to the first embodiment.

As shown in FIG. 3, the edge device 4 includes a CPU 41, a transmitter42, a receiver 43, and the database 44. The CPU 41 is a processor thatperforms various functions and controls the entire operation of the edgedevice 4 by operating according to a program prepared in advance. TheCPU 41 functions as a controller 411 and a connection establishment unit412.

The database 44 stores observed data 441, processed data 442, and dataanalysis algorithm data 443. Here, the database 44 may further storedata regarding abnormal states that have occurred in the past or thelike. The database 44 is a mass storage device (nonvolatile memory)provided in the edge device 4 and is, for example, a hard disk drive(HDD) or a solid state drive (SSD). The database 44 is also referred toas an auxiliary storage device, in which acquired information isrecorded.

The observed data 441 is, for example, vibration data observed by thesensor 321 of the plant facility 3. Here, the observed data 441 may bestored in a state of being organized as per date or time. The processeddata 442 is data created by the controller 411 performing atransformation process such as a fast Fourier transform (FFT) on theobserved data 441. Here, the transformation process is not limited tothe above and may be another process. The data analysis algorithm data443 is data used for abnormality detection.

The controller 411 performs a transformation process on the observeddata 441. The controller 411 stores the transformed data as processeddata 442. The controller 411 executes processing for determining whetheror not an abnormality has been detected using the processed data 442 onthe basis of a prediction algorithm stored in the data analysisalgorithm data 443. The controller 411 may also execute processing fordetermining whether or not an abnormality has been detected using theobserved data 441 on the basis of the prediction algorithm stored in thedata analysis algorithm data 443. Examples of the abnormality detectionmethod include the following methods.

Specifically, when there is a sufficient amount of observed data 441,the controller 411 determines which abnormal state the current state isclose to among abnormal states that have occurred in the past, forexample, by the k-nearest neighbor method on the basis of the processeddata 442. Next, the controller 411 compares a representative featureamount of the determined abnormal state with a corresponding featureamount of the current state and determines whether or not the currentstate is abnormal.

When there is not a sufficient amount of observed data 441, thecontroller 411 compresses feature amounts of normal data, for example,by principal component analysis and performs clustering for each featureamount. If the data of the current state is not close to any cluster ofthe normal data, the controller 411 determines that the current state isabnormal.

In another embodiment, the controller 411 collects vibration data as theobserved data 441, performs a fast Fourier transform on the vibrationdata, and extracts frequency feature amounts of the vibration data. Thecontroller 411 stores a data space of normal data in the database 44 bythe k-nearest neighbor method on the basis of the frequency featureamounts. A threshold value is preset for the data space of normal databy the remote monitoring device 9.

Next, when the receiver 43 has received new data, the controller 411measures the distance between the data space of normal data and the newdata. The controller 411 determines that there is an abnormality whenthe measured distance exceeds a preset threshold value.

In the above abnormality determination method, setting of the “thresholdvalue” has a great influence on abnormality determination. Further,since an appropriate “threshold value” varies depending on thecharacteristics of each facility or the usage environment of thecustomer, it is necessary to update the threshold value as appropriatewhile observing the operation status and data. This update operation isperformed by accessing the edge device 4 from the remote monitoringdevice 9.

There is a possibility that the accuracy of abnormality detection can beimproved by combining the k-nearest neighbor method with anotherabnormality detection method, for example, a support vector machine or avariational Bayes technique, rather than using the k-nearest neighbormethod alone. When such a possibility is found from the collected data,the edge device 4 may be accessed from the remote monitoring device 9and updated in such a manner that an additional algorithm is added tothe current prediction algorithm.

Although two abnormality detection methods have been described above,the abnormality detection method is not limited to the above.

When it is determined that there is an abnormality, the connectionestablishment unit 412 establishes an SSH connection with a devicehaving a preset destination address for secure data transmission. Thetransmitter 42 transmits information regarding the abnormality to thepreset destination address by sFTP on the established communicationpath. However, the secure data transmission method is not limited to theabove.

After the data transmission is completed, the controller 411 cuts offthe connection for data transmission. Next, the controller 411determines whether or not the detected abnormality is severe. Examplesof the method of determining whether or not the detected abnormality issevere include the following methods.

Specifically, in the specific example of the abnormality detectionmethod described above, when there is a sufficient amount of observeddata 441, the controller 411 compares a representative feature amount ofthe determined abnormal state with a corresponding feature amount of thecurrent state. At this time, the controller 411 determines whether ornot the abnormality is severe on the basis of the magnitude of thedifference between these feature amounts.

Further, in the specific example described above, when there is not asufficient amount of observed data 441, the controller 411 performsclustering for each feature amount as described above. At this time, thecontroller 411 determines whether or not the abnormality is severe onthe basis of the magnitude of the difference between the data of thecurrent state and the normal data.

Alternatively, when a large amount of data in normal states has beenstored in the database 44, the controller 411 constructs a long plane orthe like that characterizes its data space in advance by machinelearning (for example, by the k-nearest neighbor method or a supportvector machine). When the controller 411 has obtained abnormal data, thecontroller 411 determines the degree of the abnormality on the basis ofthe distance from the “normal data space.” For example, when the featureamount is normalized, the distance is represented by 0 to 100%. Thecontroller 411 determines that the abnormality is severe when thedistance is 80% or more and that the abnormality is not severe when thedistance is less than 80%. However, the method of determining the degreeof the abnormality is not limited to the above.

When it is determined that the abnormality is severe, the connectionestablishment unit 412 establishes a remotely accessible connection withthe remote monitoring device. When a connection has been establishedbetween the edge device 4 and the remote monitoring device 9, the remotemonitoring device 9 becomes able to remotely access the edge device 4.Here, the expression “remotely access (remote access)” represents toconnect (connection) for remotely checking a state of the observationtarget device, or for enabling a remote manipulation of the observationtarget device. The remote monitoring device 9 may include a remotecontrol device for remotely controlling the observation target device, aremote data acquisition device for remotely acquiring data from theobservation target device, and the other devices regarding the remoteaccess. When an abnormality repair process is completed, the connectionestablishment unit 412 cuts off the connection.

When it is determined that the abnormality is severe, the transmitter 42notifies the remote monitoring device 9 that a severe abnormality hasbeen detected through the established connection.

On the other hand, when it is determined that the abnormality is notsevere, an on-site person in charge of the plant who has receivedinformation regarding the abnormality from the transmitter 42 accessesthe edge device 4 and investigates the cause of the abnormality on thebasis of the determined abnormal state or the analysis result. Further,the on-site person in charge of the plant performs repair work of theplant facility 3 as necessary.

The receiver 43 receives sensor values such as vibration data oroperation data from a distributed control device (DCS) and transmits thereceived data to the database 44.

The functional configuration of the edge device 4 according to the firstembodiment has been described above.

The edge device 4 may further include a display unit that displays theobserved data 441 or the processed data 442.

In the above description, specific examples of the abnormality detectionmethod have been described. However, an abnormality may be detectedusing another method stored in the data analysis algorithm data 443.

Further, when the remote monitoring device 9 remotely accesses the edgedevice 4, the remote monitoring device 9 may use a remote desktop (RDP)or another method to access it.

Process Flow of Edge Device

FIG. 4 is a diagram showing a process flow of the edge device 4according to the first embodiment.

First, the sensor 321 observes vibration data (step S101). The sensor321 transmits the observed vibration data to the edge device 4. Thereceiver 43 of the edge device 4 receives sensor values such asvibration data or operation data from a distributed control device (DCS)and transmits the received data to the database 44. The database 44stores vibration data as observed data 441. The controller 411 performsa transformation process on the observed data 441 (step S102). Thecontroller 411 stores the transformed data as processed data 442. Thecontroller 411 executes processing for determining whether or not anabnormality has been detected on the basis of the processed data 442(step S103). The controller 411 may execute processing for determiningwhether or not an abnormality has been detected using the observed data441 on the basis of a prediction algorithm stored in the data analysisalgorithm data 443. Examples of the abnormality detection method includethe following methods.

Specifically, when there is a sufficient amount of observed data 441,the controller 411 determines which abnormal state the current state isclose to among abnormal states that have occurred in the past, forexample, by the k-nearest neighbor method or the like on the basis ofthe processed data 442. Next, the controller 411 compares arepresentative feature amount of the determined abnormal state with acorresponding feature amount of the current state and determines whetheror not the current state is abnormal.

When there is not a sufficient amount of observed data 441, thecontroller 411 compresses feature amounts of normal data, for example,by principal component analysis or the like and performs clustering foreach feature amount. If the data of the current state is not close toany cluster of the normal data, the controller 411 determines that thecurrent state is abnormal.

In another embodiment, the controller 411 collects vibration data as theobserved data 441, performs a fast Fourier transform on the vibrationdata, and extracts frequency feature amounts of the vibration data. Thecontroller 411 stores a data space of normal data in the database 44 bythe k-nearest neighbor method on the basis of the frequency featureamounts. A threshold value is preset for the data space of normal databy the remote monitoring device 9.

Next, when the receiver 43 has received new data, the controller 411measures the distance between the data space of normal data and the newdata. The controller 411 determines that there is an abnormality whenthe measured distance exceeds a preset threshold value.

In the above abnormality determination method, setting of the “thresholdvalue” has a great influence on abnormality determination. Further,since an appropriate “threshold value” varies depending on thecharacteristics of each facility or the usage environment of thecustomer, it is necessary to update the threshold value as appropriatewhile observing the operation status and data. This update operation isperformed by accessing the edge device 4 from the remote monitoringdevice 9.

There is a possibility that the accuracy of abnormality detection can beimproved by combining the k-nearest neighbor method with anotherabnormality detection method, for example, a support vector machine or avariational Bayes technique, rather than using the k-nearest neighbormethod alone. When such a possibility is found from the collected data,the edge device 4 may be accessed from the remote monitoring device 9and updated in such a manner that an additional algorithm is added tothe current prediction algorithm.

Although two abnormality detection methods have been described above,the abnormality detection method is not limited to the above.

When it is determined that there is no abnormality (step S103: No), thesensor 321 returns to the observation of vibration data (step S101).

When it is determined that there is an abnormality (step S103: Yes), theconnection establishment unit 412 establishes an SSH connection with adevice having a preset destination address for secure data transmission(step S104). The transmitter 42 transmits information regarding theabnormality to the preset destination address by sFTP on the establishedcommunication path (step S105). However, the secure data transmissionmethod is not limited to the above.

After the data transmission is completed, the controller 411 cuts offthe connection for data transmission (step S106). Next, the controller411 determines whether or not the detected abnormality is severe (stepS107). Examples of the method of determining whether or not the detectedabnormality is severe include the following methods.

Specifically, in the specific example of step S103 described above, whenthere is a sufficient amount of observed data 441, the controller 411compares a representative feature amount of the determined abnormalstate with a corresponding feature amount of the current state. At thistime, the controller 411 determines whether or not the abnormality issevere on the basis of the magnitude of the difference between thesefeature amounts.

Further, in the specific example of step S103 described above, whenthere is not a sufficient amount of observed data 441, the controller411 performs clustering for each feature amount as described above. Atthis time, the controller 411 determines whether or not the abnormalityis severe using the magnitude of the difference between the data of thecurrent state and the normal data.

Alternatively, when a large amount of data in normal states has beenstored in the database 44, the controller 411 constructs a long plane orthe like that characterizes its data space in advance by machinelearning (for example, by the k-nearest neighbor method or a supportvector machine). When the controller 411 has obtained abnormal data, thecontroller 411 determines the degree of the abnormality on the basis ofthe distance from the “normal data space.” For example, when the featureamount is normalized, the distance is represented by 0 to 100%. Thecontroller 411 determines that the abnormality is severe when thedistance is 80% or more and that the abnormality is not severe when thedistance is less than 80%. However, the method of determining the degreeof the abnormality is not limited to the above.

When it is determined that the abnormality is severe (step S107: Yes),the connection establishment unit 412 establishes a remotely accessibleconnection with the remote monitoring device 9 (step S108). Thetransmitter 42 notifies the remote monitoring device 9 that a severeabnormality has been detected through the established connection. When aconnection has been established between the edge device 4 and the remotemonitoring device 9, the remote monitoring device 9 becomes able toremotely access the edge device 4.

The remote monitoring device 9 remotely accesses the edge device 4 andinvestigates the cause of the abnormality on the basis of the determinedabnormal state or the analysis result (step S109). A person in charge ofthe remote monitoring device 9 contacts an on-site person in charge ofthe plant as necessary and instructs the on-site person in charge of theplant to perform repair work of the plant facility 3. After the repairprocess is completed, the connection establishment unit 412 cuts off theconnection (step S110).

When it is determined that the abnormality is not severe (step S107:No), the on-site person in charge of the plant who has receivedinformation regarding the abnormality in step S105 accesses the edgedevice 4 and investigates the cause of the abnormality on the basis ofthe determined abnormal state or the analysis result. Further, theon-site person in charge of the plant performs repair work of the plantfacility 3 as necessary.

Details of the process from when the sensor 321 observes the vibrationdata until the processing regarding the abnormality is completed havebeen described above.

The edge device 4 may also include a display unit that displays theobserved data 441 or the processed data 442.

Operation and Advantages

The edge device 4 according to the first embodiment includes thecontroller 411 that detects an abnormality on the basis of the processeddata 442 stored in the database 44 and the connection establishment unit412 that establishes a connection with the remote monitoring device 9such that the remote monitoring device 9 is able to remotely access theedge device 4 when the controller 411 has detected an abnormality.

Thus, in the first embodiment, only when an abnormality has occurred inthe plant, the edge device 4 is made remotely accessible from the remotemonitoring device such that appropriate countermeasures can be takeneven if a person in charge of repair does not go to the site.

Modifications of First Embodiment

Although the edge device 4 according to the first embodiment has beendescribed in detail above, specific modes of the edge device 4 are notlimited to those described above and various design changes or the likecan be made without departing from the scope of the invention.

First Modification of First Embodiment

For example, the edge device 4 according to the first embodiment hasbeen described with reference to FIG. 4 such that, when it is determinedthat there is an abnormality (step S103: Yes), the transmitter 42transmits information regarding the abnormality to the presetdestination address by sFTP on the established secure communicationpath.

Here, as a first modification of the first embodiment, the controller411 may distinguish the detected abnormality among mild to moderateabnormalities. For example, when a large amount of data in normal stateshas been stored in the database 44, the controller 411 constructs a longplane or the like that characterizes its data space in advance bymachine learning (for example, by the k-nearest neighbor method or asupport vector machine). When the controller 411 has obtained abnormaldata, the controller 411 determines the degree of the abnormality on thebasis of the distance from the “normal data space.” For example, whenthe feature amount is normalized, the distance is represented by 0 to100%. The controller 411 determines that the abnormality is severe whenthe distance is 80% or more, the abnormality is moderate when thedistance is 50% to 80%, the abnormality is mild when the distance is 20%to 50%, and there is no abnormality when the distance is less than 20%.However, the method of determining the degree of the abnormality is notlimited to the above.

Each degree of the abnormality is associated with a preset destinationaddress. The transmitter 42 may transmit preset data content to adestination address corresponding to the degree of the abnormalitydetermined by the controller 411. For example, when the detectedabnormality is mild, the transmitter 42 may transmit informationregarding the abnormality only to the address of a PC (5A) of an on-siteperson in charge of the plant. Further, when the detected abnormality ismoderate, the transmitter 42 may transmit information regarding theabnormality to both the address of the PC (5A) of the on-site person incharge of the plant and the address of a PC (5B) of an on-site managerwho is his or her supervisor.

When the destination address corresponding to the degree of theabnormality determined by the controller 411 is an address on the remotemonitoring device 9 side, the connection establishment unit 412 mayestablish a connection with the remote monitoring device 9. At thistime, the transmitter 42 notifies the destination address correspondingto the degree of the abnormality on the remote monitoring device 9 sidethat the abnormality has been detected through the establishedconnection. A person in charge having a destination addresscorresponding to the degree of the abnormality on the remote monitoringdevice 9 side remotely accesses the edge device 4 and investigates thecause of the abnormality on the basis of the determined abnormal stateor the analysis result. The person in charge on the remote monitoringdevice 9 side contacts the on-site person in charge of the plant asnecessary and instructs the on-site person in charge of the plant toperform repair work of the plant facility 3. After the repair process iscompleted, the connection establishment unit 412 cuts off theconnection.

Thus, depending on the type of the abnormality, information istransmitted only to those who need to know of the abnormality.Therefore, it is possible not only to reduce the annoyance to those whodo not need to know of the abnormality but also to complete the repairprocess quickly.

It has been described above that, when an abnormality has been detected,data is transmitted to a destination address corresponding to the degreeof the detected abnormality. Here, destination addresses to which datais to be transmitted are not limited to the addresses of the customerPCs 5A and 5B and the address of the remote monitoring device 9 sidedescribed above.

Second Modification of First Embodiment

For example, the edge device 4 according to the first embodiment hasbeen described with reference to FIG. 4 such that, when it is determinedthat the detected abnormality is not severe (step S104: No), thetransmitter 42 transmits information regarding the abnormality to thepreset destination address.

Here, as a second modification of the first embodiment, the controller411 may distinguish the detected abnormality among mild to moderateabnormalities. Each degree of the abnormality is associated with presetdata content. The transmitter 42 may transmit data content correspondingto the degree of the abnormality determined by the controller 411 to apreset destination address. For example, when the detected abnormalityis mild, the transmitter 42 may transmit data of a preset period in theprocessed data 442 to a specific address. Specifically, the transmitter42 may transmit only data on the day when the abnormality occurred inthe processed data 442 to the address of the PC (5A) of the on-siteperson in charge of the plant. Further, when the detected abnormality ismoderate, the transmitter 42 may transmit data of 10 days from the datewhen the abnormality occurred in the processed data 442 to the addressof the PC (5A) of the on-site person in charge of the plant.

Thus, a person who needs to know of the abnormality can know of onlydata necessary for repair according to the degree of the abnormality,and therefore the repair process can be completed quickly.

It has been described above that, when an abnormality has been detected,data content corresponding to the degree of the detected abnormality istransmitted to a preset destination address. Here, the data content tobe transmitted is not limited to data content classified according totime as described above and may be data content classified according tothe importance of data, the size of data, or the like.

The first and second modifications may also be combined such that datacontent corresponding to the degree of the abnormality is transmitted toa destination address corresponding to the degree of the abnormality.

Third Modification of First Embodiment

For example, the router 6 according to the first embodiment has beendescribed with reference to FIG. 1 such that the router 6 functions as acommunication path that connects the plant system 2 and the externalnetwork 7.

Here, as a third modification of the first embodiment, a power source ofthe router 6 may be normally off. Then, the connection establishmentunit 412 may activate the router 6 and establish an only connection withthe external network only when the controller 411 has detected a severeabnormality. After a repair process is completed, the connectionestablishment unit 412 may turn off the power of the router 6 and cutoff the connection.

Thus, the connection between the edge device 4 and the external network7 is cut off unless a severe abnormality occurs in the plant machine 32,and therefore the plant system 2 can be maintained in a more securestate.

Second Embodiment

Hereinafter, an edge device 4 according to a second embodiment will bedescribed with reference to FIGS. 5 and 6.

Overall Configuration

FIG. 5 is a diagram showing an overall configuration of a connectionestablishment system 1 including the edge device according to the secondembodiment.

As shown in FIG. 5, the connection establishment system 1 according tothe second embodiment includes a data analysis algorithm database 10 inaddition to the components of the first embodiment. Accordingly, thecomponents other than the data analysis algorithm database 10 includedin the connection establishment system 1 according to the secondembodiment are configured and function in the same manner as those ofthe connection establishment system 1 according to the first embodimentunless otherwise stated.

The data analysis algorithm database 10 stores at least a predictionalgorithm used when the controller 411 detects abnormalities.

Functional Configuration of Edge Device

The components included in the edge device 4 according to the secondembodiment are configured and function in the same manner as those ofthe edge device 4 according to the first embodiment unless otherwisestated.

The controller 411 calculates a predicted value on the basis of the dataanalysis algorithm data 443. The controller 411 calculates a predictionerror that is the difference between an observed value stored in theobserved data 441 and the predicted value. The controller 411 determineswhether or not the prediction error is greater than a preset allowablevalue.

When the prediction error is not greater than the preset allowablevalue, the controller 411 returns to the observation of vibration data.

When the prediction error is greater than the preset allowable value,the connection establishment unit 412 establishes a connection with theremote monitoring device. The transmitter 42 transmits informationregarding the prediction error to the remote monitoring device 9 throughthe established connection.

The remote monitoring device 9 updates the prediction algorithm storedin the data analysis algorithm database 10 on the basis of the receivedinformation regarding the prediction error. The remote monitoring device9 transmits the updated prediction algorithm to the receiver 43.

The database 44 updates the data analysis algorithm data 443 on thebasis of the updated prediction algorithm. After the update process iscompleted, the connection establishment unit 412 cuts off theconnection.

In the above method of updating the prediction algorithm, setting of the“allowable value” has a great influence on abnormality determination.Further, since an appropriate “allowable value” varies depending on thecharacteristics of each facility or the usage environment of thecustomer, it is necessary to update the threshold value as appropriatewhile observing the operation status and data. This update operation isperformed by accessing the edge device 4 from the remote monitoringdevice 9.

There is a possibility that the accuracy of abnormality detection can befurther improved by combining the k-nearest neighbor method with anotherabnormality detection method, for example, a support vector machine or avariational Bayes technique or the like, rather than using the k-nearestneighbor method alone. When such a possibility is found from thecollected data, the edge device 4 may be accessed from the remotemonitoring device 9 and updated in such a manner that an additionalalgorithm is added to the current prediction algorithm.

Process Flow of Edge Device

FIG. 6 is a diagram showing a process flow of the edge device accordingto the second embodiment.

The process flow of the edge device 4 according to the second embodimentshown in FIG. 6 is a process flow for updating the prediction algorithmused for abnormality detection. This process flow is executed beforestep S101 of the edge device 4 according to the first embodiment.Alternatively, this process flow is executed in parallel when steps S101to S110 are executed.

First, the sensor 321 observes vibration data (step S201). The sensor321 transmits the observed vibration data to the edge device 4. Thereceiver 43 of the edge device 4 receives sensor values such asvibration data or operation data from a distributed control device (DCS)and transmits the received data to the database 44. The database 44stores vibration data as observed data 441. The controller 411calculates a predicted value on the basis of the data analysis algorithmdata 443 (step S202). The controller 411 calculates a prediction errorthat is the difference between the observed value stored in the observeddata 441 and the predicted value. The controller 411 determines whetheror not the prediction error is greater than a preset allowable value(step S203).

When the prediction error is not greater than the preset allowable value(step S203: No), the controller 411 returns to the observation ofvibration data (step S201).

When the prediction error is greater than the preset allowable value(step S203: Yes), the connection establishment unit 412 establishes aconnection with the remote monitoring device (step S204). Thetransmitter 42 transmits information regarding the prediction error tothe remote monitoring device 9 through the established connection (stepS205). The remote monitoring device 9 updates the prediction algorithmstored in the data analysis algorithm database 10 on the basis of thereceived information regarding the prediction error. The remotemonitoring device 9 transmits the updated prediction algorithm to thereceiver 43. The database 44 updates the data analysis algorithm data443 on the basis of the updated prediction algorithm (step S206). Afterthe update process is completed, the connection establishment unit 412cuts off the connection (step S207).

It has been described above that, when the prediction error is greaterthan the preset allowable value, the information regarding theprediction error is transmitted to the remote monitoring device 9. Here,the transmitter 42 may be configured to, when transmitting theinformation regarding the prediction error, transmit related informationstored in edge devices in a plurality of plant facilities including theplant facility 3 to the remote monitoring device 9.

Operation and Advantages

The edge device 4 according to the second embodiment includes thecontroller 411 that detects an abnormality on the basis of data storedin the database 44 and the connection establishment unit 412 thatestablishes a connection with the remote monitoring device 9 such thatthe remote monitoring device 9 is able to remotely access the edgedevice 4 when the controller 411 has detected an abnormality, whereinthe data stored in the database 44 stores an observed value and aprediction algorithm, the controller 411 determines whether or not anerror between a predicted value based on the prediction algorithm andthe observed value is greater than an allowable value, the connectionestablishment unit 412 establishes a connection with the remotemonitoring device 9 when the error is greater than the allowable value,the edge device 4 further includes the transmitter 42 that transmitsinformation regarding the error to the remote monitoring device 9 andthe receiver 43 that receives, from the remote monitoring device 9,update information of the prediction algorithm that the remotemonitoring device 9 has generated using the information regarding theerror, and the controller 411 updates the prediction algorithm using theupdate information.

Thus, it is possible to easily and securely perform regular maintenanceof the prediction algorithm used for abnormality detection. Further, aservice provider on the remote monitoring device 9 side can provide ahighly reliable prediction algorithm. Further, since know-how forupdating the prediction algorithm does not need to be placed inequipment on the edge device 4 side, know-how leakage can be prevented.

Modifications of Second Embodiment

Although the edge device 4 according to the second embodiment has beendescribed in detail above, specific modes of the edge device 4 are notlimited to those described above and various design changes or the likecan be made without departing from the scope of the invention.

First Modification of Second Embodiment

For example, the edge device 4 according to the second embodiment hasbeen described with reference to FIG. 6 such that, after the connectionis established (step S204), the transmitter 42 transmits the informationregarding the prediction error to the remote monitoring device 9 throughthe established connection.

Here, as a first modification of the second embodiment, the transmitter42 may notify the remote monitoring device 9 that the prediction erroris greater than the allowable value, instead of transmitting theinformation regarding the prediction error to the remote monitoringdevice 9. Next, the remote monitoring device 9 remotely accesses theedge device 4, reads or searches for the information regarding theprediction error and the data analysis algorithm data 443, and updatesthe data analysis algorithm data 443 in the edge device 4. After theupdate process is completed, the connection establishment unit 412 cutsoff the connection.

Thus, it is possible to easily and securely perform regular maintenanceof the prediction algorithm used for abnormality detection. Further, aservice provider on the remote monitoring device 9 side can provide ahighly reliable prediction algorithm. Further, since know-how forupdating the prediction algorithm does not need to be placed inequipment on the edge device 4 side, know-how leakage can be prevented.

It has been described above that, after remotely accessing the edgedevice 4, the remote monitoring device 9 reads or searches for theinformation regarding the prediction error and the data analysisalgorithm data 443 and updates the data analysis algorithm data 443 inthe edge device 4. Here, when updating the data analysis algorithm data443 in the edge device 4, the remote monitoring device 9 may furtherperform data analysis in the edge device 4 and perform the update.

Second Modification of Second Embodiment

For example, the edge device 4 according to the second embodiment hasbeen described with reference to FIG. 6 such that, when the predictionerror is greater than the allowable value (step S203: Yes), theinformation regarding the prediction error is transmitted to the remotemonitoring device 9.

Here, as a second modification of the second embodiment, the connectionestablishment unit 412 may establish a connection with the remotemonitoring device 9 and transmit information regarding the predictionerror to the remote monitoring device 9 at regular intervals, ratherthan only when the prediction error is greater than the allowable value.For example, the connection establishment unit 412 may establish aconnection with the remote monitoring device 9 and transmit informationregarding the prediction error to the remote monitoring device 9 at atime preset by a company that owns the plant system 2. Alternatively,the connection establishment unit 412 may establish a connection withthe remote monitoring device 9 and transmit information regarding theprediction error to the remote monitoring device 9 in response to anoperation of the user of the customer PC 5A.

Thus, the prediction algorithm stored in the data analysis algorithmdata 443 can be maintained in a state with higher reliability.

Although some embodiments of the present disclosure have been described,these embodiments are all presented by way of example and are notintended to limit the scope of the invention. These embodiments can beimplemented in various other forms, and various omissions,substitutions, and changes can be made without departing from the spiritof the invention. These embodiments and modifications thereof areincluded in the scope or spirit of the invention as well as in the scopeof the invention described in the claims and their equivalents.

The procedures of the above processes of the CPU 41 are stored in acomputer-readable recording medium in the form of a program and theabove processes are performed by the CPU 41 reading and executing theprogram. Here, the “computer-readable recording medium” refers to amagnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, asemiconductor memory, or the like. The computer program may also bedelivered to the computer via a communication line and the computer (CPU41) which has received the program may execute the program.

The program may be one for realizing some of the above-describedfunctions. The program may also be a so-called differential file(differential program) which can realize the above-described functionsin combination with a program already recorded in the computer system.Further, the computer (CPU 41) may be composed of a single computer ormay be composed of a plurality of computers that are communicativelyconnected.

According to a second aspect of the present disclosure, the edge devicefurther includes a transmitter configured to transmit data regarding theobservation target device or information regarding the abnormality.

According to a third aspect of the present disclosure, the controller isconfigured to determine a degree of the abnormality on the basis of thedata regarding the observation target device, the connectionestablishment unit is configured to establish the connection when thedegree of the abnormality determined by the controller is severe, andthe transmitter is configured to transmit the information regarding theabnormality to a preset destination address when the degree of theabnormality determined by the controller is not severe.

According to a fourth aspect of the present disclosure, each degree ofthe abnormality is associated with a preset destination address, and thetransmitter is configured to transmit preset data content to adestination address corresponding to the degree of the abnormalitydetermined by the controller.

According to a fifth aspect of the present disclosure, each degree ofthe abnormality is associated with preset data content, and thetransmitter is configured to transmit data content corresponding to thedegree of the abnormality determined by the controller to a presetdestination address.

According to a sixth aspect of the present disclosure, each degree ofthe abnormality is associated with a preset period, and the transmitteris configured to transmit data of a period corresponding to the degreeof the abnormality determined by the controller to a preset destinationaddress.

According to a seventh aspect of the present disclosure, the dataregarding the observation target device stores an observed value and aprediction algorithm, the controller is configured to determine whetheror not an error between a predicted value based on the predictionalgorithm and the observed value is greater than an allowable value, theconnection establishment unit is configured to establish a connectionwith the remote monitoring device when the error is greater than theallowable value, the transmitter is configured to transmit informationregarding the error to the remote monitoring device, the edge devicefurther includes a receiver configured to receive, from the remotemonitoring device, update information of the prediction algorithm thatthe remote monitoring device has generated using the informationregarding the error, and the controller is configured to update theprediction algorithm using the update information.

According to an eighth aspect of the present disclosure, the dataregarding the observation target device stores an observed value and aprediction algorithm, the controller is configured to determine whetheror not an error between a predicted value based on the predictionalgorithm and the observed value is greater than an allowable value, theconnection establishment unit is configured to establish a connectionwith the remote monitoring device when the error is greater than theallowable value, the transmitter is configured to transmit notificationregarding the error to the remote monitoring device, such that theremote monitoring device is able to remotely access the edge device andto update the prediction algorithm using information regarding theerror.

According to a ninth aspect of the present disclosure, the edge deviceis further connected to a router configured to establish a connectionwith an external network, power of the router is normally off, and theconnection establishment unit is configured to activate the router andestablish a connection with the external network when the controller hasdetected the abnormality

According to a tenth aspect of the present disclosure, the edge devicefurther includes a display unit configured to display data regarding theobservation target device.

According to an eleventh aspect of the present disclosure, a connectionestablishment system includes a remote monitoring device, and the edgedevice according to the first aspect of the present disclosure.

According to a twelfth aspect of the present disclosure, a connectionestablishment method includes detecting an abnormality on the basis ofdata regarding an observation target device, and establishing aconnection with a remote monitoring device such that the remotemonitoring device is able to remotely access the edge device when theabnormality has been detected and the detected abnormality has beendetermined to match a predetermined condition, wherein the remote accessis a connection for remotely checking a state of the observation targetdevice, or for enabling a remote manipulation of the observation targetdevice.

According to a thirteenth aspect of the present disclosure, anon-transitory computer-readable medium that stores a program causes acomputer of an edge device to execute detecting an abnormality on thebasis of data regarding an observation target device, and establishing aconnection with a remote monitoring device such that the remotemonitoring device is able to remotely access the edge device when theabnormality has been detected and the detected abnormality has beendetermined to match a predetermined condition, wherein the remote accessis a connection for remotely checking a state of the observation targetdevice, or for enabling a remote manipulation of the observation targetdevice.

The components in the above embodiments can be appropriately replacedwith known components without departing from the spirit of the presentdisclosure. The technical scope of the present disclosure is not limitedto those of the above embodiments and various modifications can be madewithout departing from the spirit of the present disclosure.

REFERENCE SIGNS LIST

1 Connection establishment system

2 Plant system

3 Plant facility

31 Control device

32 Plant machine

321 Sensor

4 Edge device

41 CPU

411 Controller

412 Connection establishment unit

42 Transmitter

43 Receiver

44 Database

441 Observed data

442 Processed data

443 Data analysis algorithm data

5A, 5B Customer PC

6 Router

7 External network

8 Connection server

9 Remote monitoring device

10 Data analysis algorithm database

1. An edge device comprising: a controller configured to detect anabnormality on the basis of data regarding an observation target device;and a connection establishment unit configured to establish a connectionwith a remote monitoring device such that the remote monitoring deviceis able to remotely access the edge device when the controller hasdetected the abnormality and determined that the detected abnormalitymatches a predetermined condition, wherein the remote access is aconnection for remotely checking a state of the observation targetdevice, or for enabling a remote manipulation of the observation targetdevice.
 2. The edge device according to claim 1, further comprising atransmitter configured to transmit data regarding the observation targetdevice or information regarding the abnormality.
 3. The edge deviceaccording to claim 2, wherein the controller is configured to determinea degree of the abnormality on the basis of the data regarding theobservation target device, the connection establishment unit isconfigured to establish the connection when the degree of theabnormality determined by the controller is severe, and the transmitteris configured to transmit the information regarding the abnormality to apreset destination address when the degree of the abnormality determinedby the controller is not severe.
 4. The edge device according to claim2, wherein each degree of the abnormality is associated with a presetdestination address, and the transmitter is configured to transmitpreset data content to a destination address corresponding to the degreeof the abnormality determined by the controller.
 5. The edge deviceaccording to claim 2, wherein each degree of the abnormality isassociated with preset data content, and the transmitter is configuredto transmit data content corresponding to the degree of the abnormalitydetermined by the controller to a preset destination address.
 6. Theedge device according to claim 2, wherein each degree of the abnormalityis associated with a preset period, and the transmitter is configured totransmit data of a period corresponding to the degree of the abnormalitydetermined by the controller to a preset destination address.
 7. Theedge device according to claim 2, wherein the data regarding theobservation target device stores an observed value and a predictionalgorithm, the controller is configured to determine whether or not anerror between a predicted value based on the prediction algorithm andthe observed value is greater than an allowable value, the connectionestablishment unit is configured to establish a connection with theremote monitoring device when the error is greater than the allowablevalue, the transmitter is configured to transmit information regardingthe error to the remote monitoring device, the edge device furthercomprises a receiver configured to receive, from the remote monitoringdevice, update information of the prediction algorithm that the remotemonitoring device has generated using the information regarding theerror, and the controller is configured to update the predictionalgorithm using the update information.
 8. The edge device according toclaim 2, wherein the data regarding the observation target device storesan observed value and a prediction algorithm, the controller isconfigured to determine whether or not an error between a predictedvalue based on the prediction algorithm and the observed value isgreater than an allowable value, the connection establishment unit isconfigured to establish a connection with the remote monitoring devicewhen the error is greater than the allowable value, the transmitter isconfigured to transmit notification regarding the error to the remotemonitoring device, such that the remote monitoring device is able toremotely access the edge device and to update the prediction algorithmusing information regarding the error.
 9. The edge device according toclaim 1, wherein the edge device is further connected to a routerconfigured to establish a connection with an external network, power ofthe router is normally off, and the connection establishment unit isconfigured to activate the router and establish a connection with theexternal network when the controller has detected the abnormality. 10.The edge device according to claim 1, further comprising a display unitconfigured to display data regarding the observation target device. 11.A connection establishment system comprising: a remote monitoringdevice; and the edge device according to claim
 1. 12. A connectionestablishment method comprising: detecting an abnormality on the basisof data regarding an observation target device; and establishing aconnection with a remote monitoring device such that the remotemonitoring device is able to remotely access the edge device when theabnormality has been detected and the detected abnormality has beendetermined to match a predetermined condition, wherein the remote accessis a connection for remotely checking a state of the observation targetdevice, or for enabling a remote manipulation of the observation targetdevice.
 13. A non-transitory computer-readable medium that stores aprogram causing a computer of an edge device to execute: detecting anabnormality on the basis of data regarding an observation target device;and establishing a connection with a remote monitoring device such thatthe remote monitoring device is able to remotely access the edge devicewhen the abnormality has been detected and the detected abnormality hasbeen determined to match a predetermined condition, wherein the remoteaccess is a connection for remotely checking a state of the observationtarget device, or for enabling a remote manipulation of the observationtarget device.